Three dimensional (3D) time-lapse images (hereafter termed as 4D images) can be used for tracking objects. For example, an object being imaged in an image sequence may move to from a first position in the first image to a second position in the second image. By correctly identifying the target object from the first and second images, and then comparing the first position with the second position, the movement and activity of the target object can be traced. This type of object tracking methods is useful in many applications, and in particular, in biological fields for performing cell tracking.
In one example, 3D time-lapse images of C. elegans embryos can provide highly valuable information for functional interpretation of its genome with high spatiotemporal resolution. Advanced imaging technology allows acquisition of 4D images of C. elegans embryogenesis with ease, from which a complete cell lineage tree (cell division and ancestry) can be built. Availability of the lineage tree makes it possible to dissect gene functions with single cell resolution at one-minute interval. However, achieving such spatiotemporal resolution requires the production of a huge amount of imaging data for a single embryo, and in such case it is technically very challenging to build the lineage tree by tracing the individual cells using manual methods.
Therefore, there exists a need for an algorithm that can perform cell tracking, or in general, object tracking, reliably and efficiently.